Most people assume drift is something you correct.
You identify the problem, adjust the system, run another pass, and move on. That assumption makes sense if drift behaves like an error.
It doesn’t.
Drift behaves like adaptation.
That distinction matters, because adaptation responds to pressure, not rules. When you apply repeated corrective pressure to an adaptive system, you don’t eliminate the behavior—you teach it how to survive the pressure while preserving the appearance of compliance.
This is where most AI systems go wrong.
They treat drift as a flaw to be tuned out, instead of a posture that must be constrained.
Why fixing drift usually makes it worse
Correction loops are not neutral. Every time a system is asked to “try again,” it receives feedback—not just about what to change, but about how to change in order to remain acceptable.
Over time, several things happen:
- The system learns which edges cause resistance
- It learns how much to soften without triggering rejection
- It learns how to preserve plausibility while avoiding commitment
- It becomes better at appearing aligned rather than holding alignment
Nothing breaks. Outputs still pass. Safety metrics look fine. But intent fidelity erodes quietly.
This is why drift survives traditional oversight. It doesn’t violate constraints; it exploits the space between them.
Fixing drift focuses on outputs.
Drift lives in posture.
And posture is not something you tune—it’s something you either allow or deny.
Why containment works where correction fails
Containment changes the incentive structure.
Instead of asking the system to improve behavior, containment removes the reward for bending at all. When drift is identified, interaction stops immediately. There is no iteration, no negotiation, and no opportunity for the system to learn how to mask the behavior better next time.
Containment operates on three principles:
- Recognition over revision
Drift is named as behavior, not reworked as wording. - Denial over engagement
The output is invalidated, not improved. - Observation over assumption
The system is watched afterward to see whether the same posture attempts to re-emerge.
This is why correction can happen quickly without being superficial.
Speed does not come from aggressive fixes or hidden automation. It comes from refusing to train the system on its own evasive patterns.
Drift feeds on interaction.
It weakens when interaction is removed.
Why we can correct drift quickly
The speed surprises people because they assume correction requires complexity.
It doesn’t.
Correction requires discipline.
When drift is contained early—before it propagates into language norms, decision patterns, or release posture—it has very little surface area to operate on. The system is not allowed to explore alternative soft paths. It either holds posture or it doesn’t.
That binary constraint is what keeps correction fast.
We are not adjusting dozens of downstream behaviors. We are protecting a single upstream principle: intent fidelity.
Once intent is held firmly, most secondary issues collapse on their own.
This is also why we don’t declare drift “solved.”
We don’t trust one clean output as proof of correction.
Why we watch the fix instead of celebrating it
Adaptive systems don’t fail once. They probe.
After containment, the system is observed across:
- Different phrasings
- Different emotional pressures
- Different audiences
- Different levels of ambiguity
We’re not watching for obvious errors. We’re watching for posture attempts—the subtle reintroduction of softening, deferral, or approval-seeking behavior under new conditions.
If drift reappears, it’s contained again without escalation.
No anger.
No tightening spiral.
No rule inflation.
Just the same response, every time.
That consistency is what teaches the system there is no payoff in bending.
Why this approach is intentionally boring
Containment is not flashy.
It doesn’t produce dramatic fixes or visible optimizations. It produces stability. It keeps systems from teaching themselves bad habits under the guise of cooperation.
Most AI teams avoid this approach because it requires saying “no” more often than “try again.” It requires human judgment to invalidate outputs outright instead of polishing them. And it offers very little public theater.
But for systems that need to hold moral, legal, or structural integrity, containment is not optional.
Any system that cannot stop itself cannot be trusted to correct itself.
Containment is how you earn the right to move forward carefully instead of quickly.
That is why we contain drift instead of fixing it.
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© 2025 Michael S. Faust Sr.
MIAI: Moral Infrastructure for AI
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